Constant Module Error_via Example Code

Users
,
#1

Hi All and thank you for responding in advance!

I am running an example code to gain the experience with a 3DCNN network of interest.

I am running the example located here! https://github.com/kcct-fujimotolab/3DCNN

Can you assist, please? been banging my head around trying to understand this error!
thank you once more!!!

Everything loaded effectively but I am constantly getting an error for which I need some clarity in order to move forward!

CODE:
import argparse
import os

import matplotlib
matplotlib.use(‘AGG’)
import matplotlib.pyplot as plt
import numpy as np

%%

from keras.datasets import cifar10

%%

from keras.layers import (Activation, Conv3D, Dense, Dropout, Flatten,
MaxPooling3D)
from keras.layers.advanced_activations import LeakyReLU
from keras.losses import categorical_crossentropy
from keras.models import Sequential
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.utils.vis_utils import plot_model
from sklearn.model_selection import train_test_split

import videoto3d
from tqdm import tqdm

#Display Output Representations
def plot_history(history, result_dir):
plt.plot(history.history[‘acc’], marker=’.’)
plt.plot(history.history[‘val_acc’], marker=’.’)
plt.title(‘model accuracy’)
plt.xlabel(‘epoch’)
plt.ylabel(‘accuracy’)
plt.grid()
plt.legend([‘acc’, ‘val_acc’], loc=‘lower right’)
plt.savefig(os.path.join(result_dir, ‘model_accuracy.png’))
plt.close()

plt.plot(history.history['loss'], marker='.')
plt.plot(history.history['val_loss'], marker='.')
plt.title('model loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.legend(['loss', 'val_loss'], loc='upper right')
plt.savefig(os.path.join(result_dir, 'model_loss.png'))
plt.close()

def save_history(history, result_dir):
loss = history.history[‘loss’]
acc = history.history[‘acc’]
val_loss = history.history[‘val_loss’]
val_acc = history.history[‘val_acc’]
nb_epoch = len(acc)

with open(os.path.join(result_dir, 'result.txt'), 'w') as fp:
    fp.write('epoch\tloss\tacc\tval_loss\tval_acc\n')
    for i in range(nb_epoch):
        fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
            i, loss[i], acc[i], val_loss[i], val_acc[i]))

#Loading Data Here
def loaddata(video_dir, vid3d, nclass, result_dir, color=False, skip=True):
files = os.listdir(video_dir)
X =
labels =
labellist = 

pbar = tqdm(total=len(files))

for filename in files:
    pbar.update(1)
    if filename == '.DS_Store':
        continue
    name = os.path.join(video_dir, filename)
    label = vid3d.get_UCF_classname(filename)
    if label not in labellist:
        if len(labellist) >= nclass:
            continue
        labellist.append(label)
    labels.append(label)
    X.append(vid3d.video3d(name, color=color, skip=skip))

pbar.close()
with open(os.path.join(result_dir, 'classes.txt'), 'w') as fp:
    for i in range(len(labellist)):
        fp.write('{}\n'.format(labellist[i]))

for num, label in enumerate(labellist):
    for i in range(len(labels)):
        if label == labels[i]:
            labels[i] = num
if color:
    return np.array(X).transpose((0, 2, 3, 4, 1)), labels
else:
    return np.array(X).transpose((0, 2, 3, 1)), labels

#initialise Options Parameters
def main():
parser = argparse.ArgumentParser(
description=‘simple 3D convolution for action recognition’)
parser.add_argument(’–batch’, type=int, default=128)
parser.add_argument(’–epoch’, type=int, default=100)
parser.add_argument(’–videos’, type=str, default=‘UCF101’,
help=‘directory where videos are stored’)
parser.add_argument(’–nclass’, type=int, default=101)
parser.add_argument(’–output’, type=str, required=True)
parser.add_argument(’–color’, type=bool, default=False)
parser.add_argument(’–skip’, type=bool, default=True)
parser.add_argument(’–depth’, type=int, default=10)
args = parser.parse_args()

img_rows, img_cols, frames = 32, 32, args.depth
channel = 3 if args.color else 1
fname_npz = 'dataset_{}_{}_{}.npz'.format(
    args.nclass, args.depth, args.skip)

vid3d = videoto3d.Videoto3D(img_rows, img_cols, frames)
nb_classes = args.nclass
if os.path.exists(fname_npz):
    loadeddata = np.load(fname_npz)
    X, Y = loadeddata["X"], loadeddata["Y"]
else:
    x, y = loaddata(args.videos, vid3d, args.nclass,
                    args.output, args.color, args.skip)
    X = x.reshape((x.shape[0], img_rows, img_cols, frames, channel))
    Y = np_utils.to_categorical(y, nb_classes)

    X = X.astype('float32')
    np.savez(fname_npz, X=X, Y=Y)
    print('Saved dataset to dataset.npz.')
print('X_shape:{}\nY_shape:{}'.format(X.shape, Y.shape))

# Define model
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 3), input_shape=(
    X.shape[1:]), border_mode='same'))
model.add(Activation('relu'))
model.add(Conv3D(32, kernel_size=(3, 3, 3), border_mode='same'))
model.add(Activation('softmax'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), border_mode='same'))
model.add(Dropout(0.25))

model.add(Conv3D(64, kernel_size=(3, 3, 3), border_mode='same'))
model.add(Activation('relu'))
model.add(Conv3D(64, kernel_size=(3, 3, 3), border_mode='same'))
model.add(Activation('softmax'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), border_mode='same'))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))

model.compile(loss=categorical_crossentropy,
              optimizer=Adam(), metrics=['accuracy'])
model.summary()
plot_model(model, show_shapes=True,
           to_file=os.path.join(args.output, 'model.png'))

X_train, X_test, Y_train, Y_test = train_test_split(
    X, Y, test_size=0.2, random_state=43)

history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), batch_size=args.batch,
                    epochs=args.epoch, verbose=1, shuffle=True)
model.evaluate(X_test, Y_test, verbose=0)
model_json = model.to_json()
if not os.path.isdir(args.output):
    os.makedirs(args.output)
with open(os.path.join(args.output, 'ucf101_3dcnnmodel.json'), 'w') as json_file:
    json_file.write(model_json)
model.save_weights(os.path.join(args.output, 'ucf101_3dcnnmodel.hd5'))

loss, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)
plot_history(history, args.output)
save_history(history, args.output)

if name == ‘main’:
main()

********************************ERROR
runfile(’/Users/mmgp/opt/Spyder Files/3DCNN-master/3dcnn.py’)
Traceback (most recent call last):

File “/Users/mmgp/opt/Spyder Files/3DCNN-master/3dcnn.py”, line 22, in
import videoto3d

ModuleNotFoundError: No module named ‘videoto3d’

1 Like
#2

Hi Mat,

Your sample is approaching 200 lines of code, nearly all of which is
irrelevant to the error you are getting. It may help you to read this:

to both help you debug your own code, and to help you get better quality
answers when you ask for help.

The error you are getting is this:

ModuleNotFoundError: No module named 'videoto3d'

but videoto3d is part of the code you are running. How did you install
the 3DCNN package? “videoto3d” is part of that package, so if it is
missing, the two most obvious explanations are:

  • you only installed part of the 3DCNN code, so it won’t work; or

  • the 3DCNN is broken, and needs to be reported to the owner as a bug.

So it seems to me that to solve your problem, we need to known how you
installed the Python code you are running.

If all you did was copy this file:

and then try to run it, that won’t work.

#3

hey SD,
Thanx for responding I kind of figured it out! it’s a bunch of files calling one another from the folder. This file, based on their instructions should be initialised 1st, then the --batch!

the directory was the issue it was pointing to another location, I am still trying to figure out the sequence for which this must be deployed for performance output to be achieved!

thanx once more

My new error********************************
error: the following arguments are required: --output